Market-based price optimization system

ABSTRACT

Disclosed is a market-based software system that will help user-retailers manage price and inventories more effectively. The system will take advantage of available price and sales data to provide pricing recommendations that will achieve a retail user&#39;s objectives. The system will offer a solution that will allow for pricing improvement shortly after installation by utilizing data that is readily available. The system will recommend price changes that help a user achieve specified objectives such as contribution, sales volume, desired margins, and the like. The system can also collect and process price and sales data on an ongoing basis, which can enable improved estimates of customer price sensitivity and performance on a category-by-category basis. This data can be used to improve further pricing decisions.

SUMMARY

Disclosed is a market-based price optimization system. To effectivelyprice any given item, analysts should consider a large number offactors. Prices should be set consistently across multiple productsoffered by the same manufacturer. This requires accounting forprice-per-unit ratios across sizes and product families that must bepriced identically. Prices across brands should account for the relativeperceived value of the various brands. The gap between the price of aprivate label product and the corresponding nationally branded productshould account for these customer perceptions. The intent to promote aproduct should also influence its price; that price will influence thecustomer's perceived value of the promotion, and the depth of a pricepromotion required to obtain the desired effect. Minor changes in pricenumerical endings may have little impact on price perceptions but mayhave a significant impact on the bottom line. Each of these factorsindividually makes effective pricing complicated and difficult. Whentaken together, they present a challenge that tier-2 retailers (thosewith under $1 Billion in annual sales) are not currently equipped toovercome.

Cross-category prices should also be consistent with a retailer'soverall performance objectives. Many retailers believe consumers look atthe prices of a set of items and form a “price image” for a category andthe store as a whole. Category pricing policies should consider the roleof the category in generating store traffic, stimulating discretionarypurchases within and across categories, and whether or not the customeris even aware of price differences among competing stores. The targetprice image for any given category may be higher or lower than others,depending on the strategy of the retailer. For example, if a retailer isin a demographic area where there are many mothers, they may want todrive traffic by having a low price image in the baby category, butdrive profits by having higher prices in the candy category. The systemallows the user-retailer to tailor the pricing policies down to thesub-category level to address their individual pricing strategies.

The system will also collect and process price and sales data on anongoing basis, which can enable improved estimates of customer pricesensitivity and performance on a category by category basis. This datawill be used to improve further pricing and inventory managementdecisions.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flow chart showing the overall process flow of the marketbased price optimization system.

FIG. 2 is an illustration of a Category Review Screen useful in thesystem.

FIG. 3 is an illustration of a Category Detail Screen useful in thesystem.

FIG. 4 is an illustration of a Product Detail Screen useful in thesystem.

FIG. 5 is an illustration of a volume margin analysis graph, with marginas the ordinate and sales volume as the abscissa, useful for anunderstanding of the invention.

FIG. 6 is an illustration of a loss function useful for an understandingof the invention.

GENERAL DESCRIPTION

The system is referred to as Market-Based Price Optimization System(MBOS) and has as characteristics the fact that it can:

be designed for rapid installation and pricing recommendations;

utilize cost, promotional patterns, retail standard brand price premium,and competitive price information to identify potential candidates forprice change;

integrate the foregoing market data with retail user objectives tosuggest price changes that are likely to increase performance relativeto user objectives and subject to user constraints;

monitor response to price changes and revise optimization methodologybased on that response; and

monitor market changes and suggests price responses.

The system uses analytic modules described in more detail subsequently.These analytics modules can be implemented in software using theteachings of this written description. The system can recommend a set ofinitial price changes using limited historical sales data. The pricechanges generated by this process can generate variability in prices ofnon-promoted products, which can, in turn, be used to estimate pricesensitivity and improve price change recommendations generated by theMarket-Based Price Optimization System. “Market Based” refers to apricing system that uses elasticity or change in sales volume as aresult of price, as well as product attributes, to recommend pricechanges.

The first three steps of the analysis to recommend price changes, seenbelow, can be conducted on a bi-monthly basis. This frequency will makeit possible to statistically estimate the impacts of the price changeson sales volume, which will yield measures of price sensitivity. Otherperiods besides bimonthly can be used. It is possible to use weekly,monthly, or periods of variable lengths in the estimation of the impactof price changes if necessary or desired.

1. Price-Sensitivity Baseline Analysis. The first step in the process isto determine if any price-sensitivity baselines exist and, if so,implement price changes that incorporate these baselines. As used inthis context, “price-sensitivity baselines” means established patternsof change in quantity sold as a result of a change in a product'sregular price. These baselines will usually not exist when entering anew retail market. However, as data are collected in a market,meta-analyses will be conducted that will suggest price variations basedon knowledge generated from historical data and results from otherretailers to generate price recommendations. As used in this context,“meta-analyses” means an analysis of data from inside and outside theindividual user-retailer, but from the same market. Over time price andsales data will be analyzed to develop estimates of demand and localprice sensitivity. These estimates will be utilized to recommend pricechanges that are likely to increase profits. For new product categoriesor markets, this step can be bypassed if desired.

2. Price-Promotion Analysis. In the absence of findings frommeta-analyses or a Market-Based Price Optimization System, which absenceindicates that there is not sufficient data in the sales history todetermine a demand function for a product, a check is done to determinewhether a brand has been frequently price-promoted in the past. If abrand has been frequently price-promoted in the past, the marketingliterature suggests the regular price of the product should beincreased. Raising the price of heavily promoted products will allowgreater margins from a given price discount or a greater perceiveddiscount with the same post-promotion price.

3. Volume-Margin Analysis. For products that are not heavily promoted,identify, possible outlier products based on deviations of a user'ssales from market norms. As used in this context, “outlier products”means products with extremely high or extremely low sales volumes.Outliers form the area around the mean for a subcategory of products.Adjustments based on this step can be bypassed when there is amplehistorical data to derive local demand estimates, although the loss fromthis analysis will still contribute to the system loss. As used in thiscontext, “loss” means disutility for the final recommended price basedon its distance from the recommended price by this particular function.Adjustments can be as follows:

(a) If the user's sales for a high margin product in a subcategory arevery low, a price reduction is recommended to increase sales andprofitability.

(b) If the user's sales of a low margin product in a subcategory arevery high, a price increase is recommended to stimulate a shift in salesto more profitable products, thereby increasing category profitability.

(c) The price changes can be a percentage based on a parameter that isinput into the system and may vary based on product category and userobjectives. For example, users may define parameters like “defaultamount” to change price using a given analytics module and/or within asubcategory e.g. five percent (5%) of diapers using the Competitor PriceModule (which module is described subsequently in FIG. 1). The parametervalue may be refined over time based on category experience.

(d) A loss function can be associated with deviations from proposedchanges.

The remaining steps in the process will be implemented, for example, ona monthly monitoring basis or when there is a change in marketconditions, such as a change in unit wholesale price or competitiveprice changes, as follows.

4. Competitor Price Analysis. This step is the first step in the monthlymonitoring process. It compares the prices (incorporating proposed pricechanges from steps 1-3, above) to the prices charged by competingretailers. Competitor prices are provided by the user, who can get themfrom their own competitive shop, through a wholesaler, throughpurchasing them from a third party, or through other appropriate means.

(a) Ideal prices relative to competitors can be obtained from the userbased on specified objectives and retail strategy. Examples ofobjectives may be a specified percentage of a competitor's price orbeing within an acceptable percentage range relative to one or morecompetitors. These criteria may vary across categories.

(b) A loss function will be attached to deviations in prices fromcompetitors accounting for the desired criteria.

(c) Price recommendations will be adjusted to minimize the total lossfrom the Baseline Price Recommendation and Sales Promotion Check (alsoknow as the Promotional Frequency) modules. The end price reduction isadjusted to minimize the sum of loss from all analytics modules.

5. Brand Equity Analysis. The next step determines whether the proposedprices of various brands are within acceptable ranges relative to areference brand. This ratio will reflect the price differential or brandequity each brand commands in the competitive retail market place. Whenthe reference brand is a private label brand then this differential maybe viewed as “the private label gap.”

(a) Prices for different brands will be collected from representativecompetitors. Price per unit values will be calculated for each brand andthe ratio of the average price per unit of the chosen brand to the priceper unit of the reference brand will be computed. The reference brandfor a subcategory is determined by sales volume—the reference brand isthe best selling national or regional brand.

(b) A loss function will be attached to deviations in price ratios fromthe competitive norms.

(c) Price recommendations will be adjusted to minimize the total lossfrom the first three steps.

6. Category Margin Analysis. The third step in the ongoing monthlyprocess will compare the projected average margin from the category withobjectives set by the retail user.

(a) Initially utilizing current quantities sold, an estimated averagecategory margin will be calculated.

(b) A loss function will be assigned to average category margins thatfall short of the target value, set by the user.

(c) Price recommendations will be adjusted to minimize the total lossfrom the first four steps

7. Price Per Unit Analysis. The fourth step in the monthly process willconsider the consistency of price per unit and markup per unitrelationships. It is expected that a product's price per unit should belower as the number of units (size) increases.

A typical average relationship between unit size and both price andmargin per unit will be calculated using regression across brands. Forexample, for cola, the expected price per ounce of a cola could be:0.013+0.0125* Coke+0.0092* Pepsi. Other brands will have differentcoefficients, based on historical sales data. This module calculates arecommended price for a product based on a regression model thatregresses sales volume against price per unit and brand. The generalform of the legit model is as follows, for the hypothetical Subcategoryof fresh water fish, with the brands catfish, trout, and bass:Volume (total units)=Intercept+Price Weight+Flag(catfish)(BrandWeight)+Flag(trout) (Brand Weight)+Flag(bass)(Brand Weight)

Where

Intercept is the constant from the regression; the baseline of expectedsales without taking any product attributes into considerationwhatsoever.

Flag indicates whether the fish in question is of the subtype inquestion; if the fish to be priced is a catfish, the catfish flag is setto 1, and all other flags indicating specific variety of fish are set tozero, and

Brand weight means the comparative importance of the specific brand. Forexample, catfish might have a weight of 0.5, where trout might be moredesirable to the public (based on historical sales data), and might havea weight of 0.7. The result is that for each type of fish in theexample, there is a compound weight of (flag x brand weight). If a fishis not of a particular type, the flag is zero, and the compound weightfor that fish type is zero—because it's not that specific type of fish.

The software loops through every brand in the subcategory, setting theappropriate flag to one, and all others to zero, depending on the brand,to determine the individual weight of each brand.

With brand weights established for each brand, expected price per unitis multiplied by the size of each unit, then discounted 3%+2% beyond thefirst increment for each size up from the smallest for each size, toensure consistent reduction in price per unit as size increases. Thepercentages refer to the drop in Price Per Unit for each size. Forexample, consider Heinz Ketchup with four sizes: 14 oz., 24 oz., 36 oz.,48 oz. The 14 oz. is $1.39, or $0.10/oz. The first increment increase,from 14→24 oz, should have a 3% decrease in price per unit, or should beno more than $0.097 per oz. The next jump, from 24→36 oz., should have afurther 2% decrease, as should the jump from 36→48 oz. The first drop is3%, each subsequent drop is 2%.

Price recommendations will be adjusted to minimize the total loss fromthe Price Recommendation, Sales Promotion, Competitor Price, VolumeMargin and Category Margin modules, each described with respect to FIG.1.

8. Price Ending Analysis. The next step in the monthly process willexamine the numerical endings of the price recommendations and suggestprice adjustments that have acceptable price endings.

(a) A loss function will be associated with price endings. For example,no loss will be associated with a price that ends with a 9 and a largeloss will be associated with a price that ends with 1. The loss functionshould account for at least two significant digits. For example a pricethat ends with 0.05 has a high loss while a price that ends with 0.095will have a substantially lower loss. Available evidence frompeer-reviewed research indicates that consumers process pricinginformation from left to right. That is, consumers see the pricedifference between $19.95 and $20.00 as considerably greater than $0.05,and this is reflected in their purchasing behavior. Hence, prices withending numbers distant from 0.95 or 0.99 will have relatively high lossvalues associated with them.

Information Presented to the User

A Category Review Screen presented to the user by the system is seen inFIG. 2. This screen presents summary information on categories to helpretailers make strategic decisions at the category level. This screenpresents information such as; revenue and profit broken down bycategory, revenue contribution and profit contribution, and unitmovement by category for use in reviewing category performance, as wellas overall store performance.

This screen will also help user-retailers identify problem categoriesthat may need more attention. The screen displays revenue contributionas well as profit contribution, which when compared with each other canreveal powerful results. User-retailers can identify underperformingcategories, review their use of inventory and shelf space, and identifysales trends in their stores across departments, categories, orsubcategories.

To analyze a specific category, the user-retailer can double click onthe desired category and all products in that category will be displayedon a Category Detail Screen, which follows. A Category Detail Screen isprovided as seen in FIG. 3 for use by the user-retailer's pricinganalyst to review the recommended prices and associated data. Using thisscreen, the user-retailer can track all the summary data for allproducts in a given category or subcategory. By clicking on anindividual product on the Category Detail Screen, the user can move tothe Product Detail Screen and access the detail information for onespecific product.

The data on the screen can be filtered and sorted. If the user-retailerwishes to only see items that have had a price change since the lastreview, only those items will be presented. Or, if the user-retaileronly wants to review items in which the system is recommending a pricechange, only those can be viewed. The data can be sorted by any of thecolumns. User-retailers may drill down for information about any givenproduct by simply clicking on an individual item. This will bring up theProduct Detail Screen.

The Product Detail Screen, an example of which is seen in FIG. 4, showsdetailed information on a specific item. If the user-retailer wishes tofurther investigate why a price change is being recommended, they canview this screen. The Product Detail Screen can show sales history,price-per-unit ratios within a product family, private label gap withassociated products, and more. In addition, the detail screen can alsoshow the prices derived from the individual analytical components,providing the user-retailer with insight into the large number ofanalytical functions performed by the system, and the impact of each onthe overall recommended price.

The relative importance, or weighting, of the analytics modules can beset by the user of the software. Additionally, the user may elect tolock a product price—that is, elect not to change a product's price forcompetitive or other reasons—and omit that particular product from theanalysis process.

If the user-retailer's analyst wishes to view detailed information on aproduct, they can double click on the item and bring up the ProductDetail Screen.

DETAILED DESCRIPTION

The overall Market-Based Price Optimization System (MBOS), describedgenerally above will now be described in more detail with reference tothe flow diagram of FIG. 1, which is an indication of process flow.

The MBOS utilizes inputs from several of its modules to generaterecommendations for the regular (non-promoted) prices on asubcategory-by subcategory basis. Each module recommends pricesindividually which are reconciled to determine the prices recommended tothe user-retailer. This can be presented via the Category Detail Screendescribed previously with respect to FIG. 3. The first three modules(Baseline Price Recommendation Module 10, Promotional Check Module 20and Volume-Margin Analysis Module 30) are driven by an analysis ofrecent sales, price, and promotion data and are recalibrated on abi-monthly or quarterly basis. The remaining modules (the CompetitivePrice Check Module 40, the Brand Equity Check Module 50, the CategoryMargin Check Module 60, the Price Unit Check Module 70 and the PriceEnding Check Module 80, are calibrated on a weekly basis, or otherwise,to correspond with a typical user-retailer's pricing cycle and are usedas a basis for refining price adjustments that periodic basis.

Each module generates price recommendations independently. For eachspecific module except the Price Ending Check Module 80, a measure isdeveloped that associates a penalty or loss with deviations between apotential final price recommendation by the overall system and the pricerecommended by the specific module. These loss functions reflect therelative importance and reliability of each module in generatingprofitable and consistent prices. The final price recommended by theMBOS 1 is the price that minimizes the sum of the losses from all of themodules, as shown in Table 1, below. TABLE 1 Loss Minimization Acrossall Modules Recommended Low High Module Price Loss Exp ToleranceBoundary Boundary Loss Baseline-Demand $3.91 1.1 5% $3.71 $4.1110.460258 Sales Promotion $3.88 1.025 3% $3.78 $3.98 22.992653 VolumeMargin $3.99 1.1 10%  $3.59 $4.39 0 Competitor Price $4.29 1.05 3% $4.16$4.42 0 Brand Equity $4.08 1.25 5% $3.88 $4.28 0 Category Margin $4.491.05 2% $4.40 $4.58 24.477347 Price Per Unit $4.09 1.075 5% $3.89 $4.290 Price Endings $4.19 1.6 0% $4.19 $4.19 0 Overall $4.19 57.930258

The weights, or relative importance, assigned to each module aredetermined by combining the user-retailer's objectives for the category,such as profit center or traffic builder (which may vary acrosscategories) with the user-retailer's overarching goals for a store,zone, or chain. These values are not determined empirically, but ratherby the user of the software.

An example of the determination of loss for each individualrecommendation by each analytic module, and the adjustment of pricerecommendations to minimize the sum of losses from all modules follows.

Loss can be calculated as follows:

For each analytics module in MBOS, there is an optimized recommendedprice. The loss function for each module is calculated via linearprogramming asLoss=(|Price(recommended[overall])-Price(recommended[module])|-Tolerance)ˆL{Min0}

where

Price (recommended[overall]) is the price based on the weighted averageof all modules,

Price (recommended[module]) is the recommended price from one specificMBOS analytic module,

Tolerance is the acceptable distance between the two prices (defined bythe user),

L is the loss exponent, adjustable within the software based on thepreference of the user-retailer, and

{Min 0} means that loss cannot be below zero. (It is a measure of stresswithin the system; the amplitude of the number (its absolute value) iswhat is important, and IS always positive.)

This is seen in the loss function graph of FIG. 6. A high value for Lfor a given analytic module means that if the overall recommended priceis distant from the recommended price generated by that individualmodule, and outside the acceptable tolerance range, there will be a highloss. Given the specific nature of retailing, users may choose differentloss exponents for different subcategories. For example, if auser-retailer is in close proximity to a competitor, they may choose avery high loss exponent for the Competitor Price Module 40. If they dothis, loss will become very high very quickly if a product's price isoutside the defined tolerance. The higher the loss exponent, the morequickly the loss escalates if the overall recommended price is far fromthe price recommended by the individual analytic module. The user hascontrol of these exponents down to the subcategory level, giving themsignificant control over the final recommendations of the MBOS.

The final recommended price from MBOS will be determined by minimizingthe total loss function values for a given product. Minimization can beperformed via well-known linear programming, a standard mathematicaltechnique, not unlike regression, identified elsewhere in this patent.

The following is a more detailed description on of the analytics modulesand their operation. The analysis to recommend changes based the firstthree steps of the process below can be conducted on a bimonthly orquarterly basis. This frequency will make it possible to statisticallyestimate the impacts of the price changes on sales volume, which willyield measures of price sensitivity.

A. Baseline-Demand Module 10: The first step in the process is todetermine if any price-sensitivity baselines exist and, if so, as at 11,implement price changes that incorporate these baselines. This isperformed in the Baseline-Demand Module 10 of FIG. 1. Baselines arederived from a meta-analysis of demand estimates from comparableretailers or, if sufficient price variability exists, from recenthistorical sales data provided by the user-retailer. An example of pricesensitivity calculation using historical sales data is seen in Table 2.TABLE 2 Example of Price Sensitivity Calculation using Historical SalesData Product Price Quantity ΔP ΔQ ΔQ/ΔP Coke, 2 Liter $2.49 63 25% 35%1.40 $1.99 97 Hz Ketchup, 42 oz $2.99 91 14%  7% 0.49 $3.49 85

In the example in Table 2, Coke sales dropped significantly when theprice was increased, where Heinz Ketchup sales did not. Coke would beconsidered a product with significant price sensitivity, where HeinzKetchup would be considered price insensitive. The threshold values forwhether or not a product is considered price sensitive can be determinedby the user at the subcategory level.

Baselines may not exist, as at 12, for example when entering a newretail market, in which case this module 10 is skipped until demand canbe estimated more reliably. After price changes have been implemented inthe category, the price variation allows demand estimation utilizing theMarket Based Optimization System. Using these estimatesprofit-maximizing prices are computed for the category, which are thenused as the basis for price recommendations subject to constraints thatprice changes will be restricted to ranges in which the demand estimatesare deemed to be reliable.

B. Promotional Frequency Check Module 20: The Promotional FrequencyCheck Module 20 checks to determine whether a brand has been frequentlyprice-promoted in the past. If the price has been frequently promoted inthe past as at 21, the regular price of the product is increased. Ifnot, as at 22, the Volume Margin Analysis Module 30 is put intooperation. Raising the price of heavily promoted products will allowgreater margins from a given price discount or a greater perceiveddiscount with the same post-promotion price. The magnitude of the priceincrease will be proportional to a measure of promotional depth andfrequency. For example, if a product is promoted every month at fiftypercent (50%) off, the price increase will be greater than if theproduct is promoted one month out of six at thirty percent (30%) off.For products that are not regularly promoted, this adjustment can bebypassed if desired. All parameters of the promotional frequencyanalytics can be determined by the end user. The assessment ofappropriate products and price adjustments can be made as follows:

If PRODUCT is promoted more frequently than N times in X weeks, thenPRICE(PRODUCT) is increased by S percent. N, X, and S are all determinedby the user of the software.

C. Volume-Margin Analysis Module 30: The objective of the Volume-MarginAnalysis Module 30 is to improve category profitability by adjustingprices in a way that improves average unit contribution in the category.Prices are increased for high volume, low margin products and reducedfor low volume, high margin products. The goal is to either induceswitching to higher margin products, or increase the profitability ofhigh volume products that have lower margins.

This module is based on the assumption that ideally, there should be apositive relationship between profitability and sales volume. Theuser-retailer would prefer to sell more products with a high profitmargin, and fewer products with a low profit margin in most cases.

Volume-Margin Analysis takes place as follows, as seen with respect toFIG. 6:

Total movement for each product in a subcategory is plotted (scattergraph—Y Axis) against Margin Dollars (Price-Cost) for each SKU. Theencircled points on the figure are merely illustrations of end points.

A “Target Line” is established as follows:Line slope=(average(Q(Top 10% SKUs by Q))-average(Q(Bottom 10% SKUs byQ)) divided by (average((P-C)(Top 10% SKUs by(P-C)))-average((P-C)(Bottom 10% SKUs by (P-C))))

Line intercept=Min P-C, Min Q

In this algorithm, Price is absolute price; it is not price per unit.

Once the target line is established, it is used as the center of thetarget range for the Volume Margin Analysis module. The target range isdefined as the target line±side(P-C)*(K)

where P is price, C is cost, and K a constant identified by the user todefine an acceptable level of tolerance for volume/margin variance.

Products with a price/volume pairing outside of the target range willhave their prices adjusted accordingly—reduced if volume is low butmarkup is high, and increased if volume is high but markup is low. Theamount of the price change is a percentage determined by the softwareuser.

In summary, data points indicate products within a subcategory, with thequantity sold graphed against the margin for that particular product. Noprice adjustment is made on those products within the acceptable bandbounded by the dotted lines as at 31 in FIG. 1; this area is defined inthe algorithm above. Products outside that area, as at 32 in FIG. 1,will have a price adjustment made to move them closer to the centerline.

(i) If a large fraction of a category's sales are generated by lowmargin products as at 33 of FIG. 1, those prices will be increased. If alarge fraction of a category's sales are generated by high marginproducts as at 34 in FIG. 1, the price will be reduced. This is a verypowerful process. By reducing the price on the highest margin items in asubcategory, one can switch consumers to those products and away fromless profitable options that may have large sales volumes. Thisadjustment is based on an expectation that consumers will eithercontinue purchasing or switch to higher margin alternatives. Anexception is assigned to those products that the user-retailer hasexecuted a “price lock” on, usually because those products are dedicatedtraffic builders, which the user-retailer believes must be priced low tomaintain a low price image.

(ii) The magnitude of price changes depends on product category orsubcategory and user objectives. The parameter value that determines themagnitude of the price changes is adjusted over time based on historicaldata within the category. It is a simple percent value, used as amultiplier against current price.

The remaining steps in the process, described below, can be executed ona weekly basis or when there is a change in market conditions, such aschanges in unit wholesale cost, or changes in competitor price. Thefirst of these modules, the Competitor Price Module 40, operatesindependently of the other modules. Competitor Price analytics do notdepend on sales history or on information from other modules withinMBOS. Competitor Price analytics consider only the prices charged for aparticular product at user-retailer-identified competitors. Theremaining modules (Brand Equity Module 50, Category Margin Check Module60, Price Per Unit Check Module 70 and Price Ending Check Module 80)utilize the recommendations of the preceding modules and adjust them toimprove internal consistency of prices within the category. Pricechanges that are more frequent than the bi-monthly or quarterlyrecalibrations of the above modules occur if: 1) competitive priceschange resulting in changes in prices from the Competitor Price Module,or 2) unit wholesale costs change, prompting changes in therecommendations from the Category Margin and Price per Unit Modules.

D. Competitor Price Module 40: This module utilizes prices obtained fromcompetitive price shops and compares potential prices with those chargedby competitors. The recommended prices are based on the stated overallstrategy of the user-retailer (such emphasizing price, service,selection, or value), and the role of the category in the execution ofthe strategy.

(i) Ideal prices relative to competitors will be obtained from the userbased on specified objectives and retail strategy. Examples ofobjectives may be a specified percentage of a competitor's price orbeing within an acceptable percentage range relative to one or morecompetitors such as at 41. These criteria may vary across categories. Ifthe price is higher than an acceptable difference as at 42, therecommendation is to lower the price. If it is lower than acceptabledifference as at 43, then the recommendation is to raise the price.

For example, consider an individual product—24-ounce Heinz Ketchup. Tocalculate the ideal price position for the user, the weight for eachcompetitor is multiplied by his or her price, to create a weightedaverage, as illustrated in Table 3. TABLE 3 Example of Creation ofWeighted Average Competitor Price Weight Price * Weight Safeway $1.7940% $.716 Riley's $1.89 30% $.567 Albertson's $1.89 20% $.378 Nugget$1.99 10% $.199 Sum — 100%  $1.86

Table 3 shows that the weighted average, based on user-identifiedimportance of each competitor, is $1.86.

This value is multiplied by the Competitor Price Index, which isidentified for each subcategory by the user. The Competitor Price Indexis simply a number that indicates where the software user wants to pricetheir goods relative to their competition. If the user wanted to price5% above competitors for a given subcategory, they would set theCompetitor Price Index at 1.05. To arrive at the recommended price for agiven product, the weighted average price is multiplied by theCompetitor Price Index. In this case, if the Competitor Price Index is1.05, the resulting Competitor Price Module recommended price would be1.05×$1.86, or $1.95.

(ii) A loss function, as described above, will be attached to deviationsin prices from those charged by competitors according to both thedesired price relative to competitors as well as the extent to which itfalls within an acceptable range. The percentage variance that definesthe acceptable range is determined by the software user. Lossesassociated with the latter criterion typically will receive greaterweight. Price recommendations for the module are set to minimize thetotal loss from these two components.

E. Brand Equity Module 50: This module determines whether the proposedprices of various brands are within acceptable ranges relative to areference brand in a subcategory. This ratio reflects the proportionalprice differential, or brand equity, each brand commands relative to areference brand as measured by prices currently charged by competingretailers (including the user). This module adjusts prices to ensureconsistency across sizes and product forms. When the product brand is aprivate label brand then this differential may be viewed as “the privatelabel gap.”

One of the product specific dimensions that retailers should monitor andoptimize is the private label gap, or the amount a private label productis priced below a nationally branded product of similar size and usage.The size of the gap, which typically ranges from 20% to 40%, is highlydebated among retailers. Private label gap targets are identified bysubcategory, based on an ad hoc review of sales data by the user at thetime of software installation. Private labels are used in the BrandEquity analytics module (which module is described subsequently inFIG. 1) as a part of a multiple regression. The system monitors thesegaps, and identifies instances where these prices are not withinuser-specified ranges. When a private label product has a price which istoo far away (as identified by the user above) from the national brandprice, the software will recommend an incremental price change of N % ofthe current price, where N is a parameter established by the user. Otherdimensions used in the analysis include price-per-unit ratios forproduct lines, and families of products that require identical pricing.

(i) Price per unit values are calculated for each brand and the ratio ofthe average price per unit of the chosen brand to the price per unit ofthe reference brand is computed. Prices per unit by brand are calculatedvia regression analysis using historical sales volume, and a flag foreach different brand within a subcategory, as follows:Price Per Unit=Intercept+Coefficient(Brand 1)+Coefficient(Brand 2) . . .+Coefficient(Brand N), where N is the number of brands in thesubcategory

where:

Intercept is the constant resulting from the regression analysis of thesales data.

Coefficient (Brand) is the price multiplier, determined by theregression analysis above, for the first brand in the subcategory.

Coefficient (Brand) is the price multiplier, determined by theregression analysis above, for the second brand in the subcategory; and

Coefficient (Brandon) is the price multiplier, determined by theregression analysis above, for the last (Nth) brand in the subcategory.

A Price Per Unit is then calculated for each product, and a recommendedprice determined by multiplying the Price Per Unit by the pack size. Ifthe recommended price varies from the current price by more than V %(where V is determined by the software user), the price is adjustedupward, as at 51 in FIG. 1, or downward, as at 52 in FIG. 1, by X %.(where X is determined by the software user). Prices that are consistentas defined by the user are recommended to have no price change, as at53.

(ii) Price recommendations will be adjusted to minimize the total lossfrom this and the preceding modules, as described previously.

F. Category Margin Module 60: This module checks to determine if theprices recommended are consistent with the user-retailer's averagemargin objectives for the category or subcategory. In the absence ofspecific user objectives, current margins (perhaps with a slightincrease depending on the category) are utilized.

The Category Margin Module 60 calculates a recommended price for aproduct in a subcategory based on a user-defined target margin. Ifsubcategory margin (margin is calculated as (price-cost)/price)) isbelow target margin, all products in the subcategory that are notexplicitly excepted from optimization via price lock are ranked(descending) by total volume. From that list, the top item's price isadjusted upwards by N % (as defined by the user) and then marginrecalculated. If the price change increases total loss for the product,the price change is discarded, and the process is repeated on the nextproduct down on the list. This is seen generally at 61 in FIG. 1. If thesubcategory margin is above the target margin, the item's price isadjusted downwards as seen at 62. Margins that are in line are notadjusted, as seen at 63.

The number of price recommendations created by this module is subject toa maximum as defined by the user.

(i) Initially utilizing current quantities sold, an estimated averagecategory margin with the new prices is calculated. An example is seen inTable 4. TABLE 4 Example of average category margin calculation ProductPrice Cost Qty Margin Oreo Cookies 32 Oz 4.99 2.99 24 40% PepperidgeFarm 3.99 2.99 17 25% Milan Cookies Average for — — 41 34.7%   Category

In this example of a two-product subcategory of cookies, the weightedaverage margin for the subcategory is calculated by taking the margin(price-cost) for all products sold, and dividing by total sales for thesubcategory. In the example provided, that's(($2.00)*24)+(($1.00)*17))=$65, divided by (24* $4.99)+(17*$3.99)=187.59, so the average category margin is 34.7%.

(ii) A loss function is assigned to average category margins that fallshort of the target value.

(iii) Price recommendations are adjusted to minimize the total loss fromthis and the preceding modules.

G. Price per Unit Module 70: This module evaluates the consistency ofprice per unit and markup per unit relationships with proposed prices.It is expected that a product's price per unit should be lower as thenumber of units (size) increases.

(i) A typical average relationship between unit size and both price andmargin per unit is calculated using regression or means across brands.

(ii) A loss function is assigned to prices that are inconsistent with amonotonically decreasing price and profit per unit relationship. Ifavailable data indicates significant deviations from industry orcategory baselines, loss for this module is increased. The loss functionplaces a greater weight on the markup per unit than on price per unit.Also, a greater loss weight is placed on prices that violate a monotonicrelationship than on prices demonstrating deviations from industry orcategory norms.

For each increasing SIZE of the same PRODUCT, a price per unit iscalculated as PRICE/NUMBER OF UNITS. For example, a 14 oz bottle ofHeinz Ketchup that costs $1.40 would have a price per unit of $1.40/14oz, or $0.10/oz. If that bottle of Ketchup has a cost of $ 0.70 perbottle, the margin per unit would be ($1.40-$0.70)/14 oz, or $0.05/oz.

If the PRICE PER UNIT is higher for a larger size container of aPRODUCT, the software recommends a higher price for the smallest sizecontainer, so that the smallest container has the highest PRICE PER UNITfor that PRODUCT. That price is set by setting the price per unit to N %above the price per unit for the next size container up, and multiplyingby the size of the container. The specific value of N is determined bythe user.

(iii) Price recommendations will be adjusted to minimize the total lossfrom the first five steps.

H. Price Endings Module 80: The final module 80 examines the endings ofthe price recommendations from the preceding modules and suggests priceadjustments that have acceptable price endings. This is done through theuse of a set of lookup tables, depending on the price of the product.

If the product costs less than $0.50, no adjustment is made to theending digits.

If the product costs between $0.50 and $1.00, price ending adjustmentsare made as seen in Table 5. TABLE 5 Price Endings Adjustments forPrices Ending Between $.51 and $1.00 Price Ending Value Price Ending(Last Two Digits) Adjustment (Add) 50 0 51 0 52 0 53 0 54 0 55 0 56 0 570 58 0.01 59 0 60 −0.01 61 0.04 62 0.03 63 0.02 64 0.01 65 0 66 0.03 670.02 68 0.01 69 0 70 −0.01 71 0.04 72 0.03 73 0.02 74 0.01 75 0 76 0.0377 0.02 78 0.01 79 0 80 −0.01 81 −0.02 82 0.07 83 0.06 84 0.05 85 0.0486 0.03 87 0.02 88 0.01 89 0 90 −0.01 91 0.07 92 0.06 93 0.06 94 0.05 950.04 96 0.03 97 0.02 98 0.01 99 0

If the recommended price above $1.00, price ending adjustments are madeas seen in Table 6. TABLE 6 Price Ending Adjustments for Price EndingsBetween $1.00 and $1.99 Price Ending Value Price Ending (Last TwoDigits) Adjustment (Add) 01 −0.02 02 −0.03 03 −0.04 04 0.05 05 0.04 060.03 07 0.02 08 0.01 09 0 10 0.09 11 0.08 12 0.07 13 0.06 14 0.05 150.04 16 0.03 17 0.02 18 0.01 19 0 20 −0.01 21 0.08 22 0.07 23 0.06 240.05 25 0.04 26 0.03 27 0.02 28 0.01 29 0 30 −0.01 31 −0.02 32 −0.03 330.06 34 0.05 35 0.04 36 0.03 37 0.02 38 0.01 39 0 40 0.09 41 0.08 420.07 43 0.06 44 0.05 45 0.04 46 0.03 47 0.02 48 0.01 49 0 50 −0.01 51−0.02 52 −0.03 53 −0.04 54 −0.05 55 −0.06 56 −0.07 57 0.02 58 0.01 59 060 −0.01 61 0.08 62 0.07 63 0.06 64 0.05 65 0.04 66 0.03 67 0.02 68 0.0169 0 70 0.09 71 0.08 72 0.07 73 0.06 74 0.05 75 0.04 76 0.03 77 0.02 780.01 79 0 80 −0.01 81 −0.02 82 −0.03 83 0.06 84 0.05 85 0.04 86 0.03 870.02 88 0.01 89 0 90 −0.01 91 0.08 92 0.07 93 0.06 94 0.05 95 0.04 960.03 97 0.02 98 0.01 99 0

The final resulting price recommendations will be adjusted to minimizethe total loss from all modules.

While the foregoing has been with reference to particular embodiments ofthe invention, it will be appreciated by those skilled in the art thatchanges in these embodiments may be made without departing from theprinciples and spirit of the invention, the scope of which is defined bythe appended claims.

1. A price optimization system for recommending price changes for products of differing brands to a user, said system comprising: an analytic module for determining whether any price-sensitivity baselines exist and, if so, implementing price changes that incorporate these baselines; an analytic module for determining whether a brand of product has been frequently price-promoted in the past and, if so, recommending that the regular price of the product be increased; an analytic module for identifying possible outlier products from market norms based on deviations of a user's sales from said market norms and adjusting the price of said outlier products based on said deviations; an analytic module for comparing the prices of a product to prices charged for said product by retailers competing with said user and recommending prices based on an overall strategy of said user and the of said product in the execution of said strategy; an analytic module for determining whether the prices of products of specific brands are within acceptable ranges relative to a reference brand; an analytic module for comparing the projected average margin from a category of products with objectives set by said user, and calculating a recommended price for said product based on said objectives; and an analytic module for calculating across brands of a product a typical average relationship between unit sizes of said product and both price and margin per unit of said product.
 2. The system of claim 1 wherein said analytic module for calculating said typical average relationship uses a mathematical model that includes an intercept, a price weight and a flag for each brand of product and, using software to loop through each brand of the product, setting the appropriate flag to one and all others to zero, depending on the brand, to determine the individual weight of each brand.
 3. A price optimization system for recommending product price changes to a user, said system comprising: an analytic module for collecting product prices for different product brands from representative competitors of said user and competitive norms for said prices; calculating price per unit values for each product brand; calculating the ratio of the average price per unit of a chosen product brand to the price per unit of a reference brand; and using said ratio to recommend to said user a product price for said chosen product.
 4. The system of claim 3 wherein said module calculates the deviation in price rations from the recommended price and attaches a loss function to said deviations.
 5. The system of claim 4 wherein said module adjusts said price recommendations to minimize the sum of said loss functions.
 6. A price optimization system for recommending product price changes to a user, said system comprising: an analytic module for calculating across brands of a product a typical average relationship between unit size of a product and both price and margin per unit of said product using a mathematical model that includes an intercept, a price weight and a flag for each product; and using software to loop through each brand of the product, setting the appropriate flag to one and all others to zero, depending on the brand, to determine the individual weight of each brand.
 7. A price optimization system for recommending product price changes to a user, said system comprising at least two analytic modules calculating price recommendations independently, each specific one of said modules developing a measure associating a loss with deviations between a potential final price recommendation by the overall system and the price recommended by the specific module.
 8. The system of claim 7 wherein the system recommends a final price for a product that minimizes the sum of the losses from all of said modules.
 9. The system of claim 7 wherein said loss function is Loss=(|Price(recommended[overall])-Price(recommended[module])|-Tolerance)ˆL {Min 0}where Price (recommended[overall]) is the price based on the weighted average of all modules, Price (recommended[module]) is the recommended price from one specific MBOS analytic module, Tolerance is the acceptable distance between the two prices (defined by the user), L is the loss exponent, adjustable within the software based on the preference of the user-retailer, and {Min 0} means that loss cannot be below zero.
 10. A price optimization system for recommending product price changes to a user, said system comprising an analytic module for recommending price changes to a plurality of products, determining the numerical endings of the prices for at least some of said recommended price changes, and providing suggested price adjustments based on said numerical endings.
 11. The system of claim 10 wherein said suggested price adjustments for numerical endings between $0.50 and $1.00 are selected from the following table according to price ending value: Price Ending Value Price Ending (Last Two Digits) Adjustment (Add) 50 0 51 0 52 0 53 0 54 0 55 0 56 0 57 0 58 0.01 59 0 60 −0.01 61 0.04 62 0.03 63 0.02 64 0.01 65 0 66 0.03 67 0.02 68 0.01 69 0 70 −0.01 71 0.04 72 0.03 73 0.02 74 0.01 75 0 76 0.03 77 0.02 78 0.01 79 0 80 −0.01 81 −0.02 82 0.07 83 0.06 84 0.05 85 0.04 86 0.03 87 0.02 88 0.01 89 0 90 −0.01 91 0.07 92 0.06 93 0.06 94 0.05 95 0.04 96 0.03 97 0.02 98 0.01 99 0


12. The system of claim 10 wherein said suggested price adjustments for numerical endings above $1.00 are selected from the following table according to price ending value: Price Ending Value Price Ending (Last Two Digits) Adjustment (Add) 01 −0.02 02 −0.03 03 −0.04 04 0.05 05 0.04 06 0.03 07 0.02 08 0.01 09 0 10 0.09 11 0.08 12 0.07 13 0.06 14 0.05 15 0.04 16 0.03 17 0.02 18 0.01 19 0 20 −0.01 21 0.08 22 0.07 23 0.06 24 0.05 25 0.04 26 0.03 27 0.02 28 0.01 29 0 30 −0.01 31 −0.02 32 −0.03 33 0.06 34 0.05 35 0.04 36 0.03 37 0.02 38 0.01 39 0 40 0.09 41 0.08 42 0.07 43 0.06 44 0.05 45 0.04 46 0.03 47 0.02 48 0.01 49 0 50 −0.01 51 −0.02 52 −0.03 53 −0.04 54 −0.05 55 −0.06 56 −0.07 57 0.02 58 0.01 59 0 60 −0.01 61 0.08 62 0.07 63 0.06 64 0.05 65 0.04 66 0.03 67 0.02 68 0.01 69 0 70 0.09 71 0.08 72 0.07 73 0.06 74 0.05 75 0.04 76 0.03 77 0.02 78 0.01 79 0 80 −0.01 81 −0.02 82 −0.03 83 0.06 84 0.05 85 0.04 86 0.03 87 0.02 88 0.01 89 0 90 −0.01 91 0.08 92 0.07 93 0.06 94 0.05 95 0.04 96 0.03 97 0.02 98 0.01 99 0


13. A price optimization process for recommending price changes for products of differing brands to a user, said process comprising: determining whether any price-sensitivity baselines exist and, if so, implementing price changes that incorporate these baselines; determining whether a brand of product has been frequently price-promoted in the past and, if so, recommending that the regular price of the product be increased; identifying possible outlier products from market norms based on deviations of a user's sales from said market norms and adjusting the price of said outlier products based on said deviations; comparing the prices of a product to prices charged for said product by retailers competing with said user and recommending prices based on an overall strategy of said user and the of said product in the execution of said strategy; determining whether the prices of products of specific brands are within acceptable ranges relative to a reference brand; comparing the projected average margin from a category of products with objectives set by said user, and calculating a recommended price for said product based on said objectives; and calculating across brands of a product a typical average relationship between unit sizes of said product and both price and margin per unit of said product.
 14. The process of claim 13 wherein said calculating said typical average relationship uses a mathematical model that includes an intercept, a price weight and a flag for each brand of product and, using software to loop through each brand of the product, setting the appropriate flag to one and all others to zero, depending on the brand, to determine the individual weight of each brand.
 15. A price optimization process for recommending product price changes to a user, said process comprising: collecting product prices for different product brands from representative competitors of said user and competitive norms for said prices; calculating price per unit values for each product brand; calculating the ratio of the average price per unit of a chosen product brand to the price per unit of a reference brand; and using said ratio to recommend to said user a product price for said chosen product.
 16. The process of claim 15 including calculating the deviation in price ratios from the recommended price and attaching a loss function to said deviations.
 17. The process of claim 16 including adjusting said price recommendations to minimize the sum of said loss functions.
 18. A price optimization process for recommending product price changes to a user, said process comprising: calculating across brands of a product a typical average relationship between unit size of a product and both price and margin per unit of said product using a mathematical model that includes an intercept, a price weight and a flag for each product; and using software to loop through each brand of the product, setting the appropriate flag to one and all others to zero, depending on the brand, to determine the individual weight of each brand.
 19. A price optimization process for recommending product price changes to a user, said process comprising at least two analytic modules calculating price recommendations independently, each specific one of said modules developing a measure associating a loss with deviations between a potential final price recommendation by the overall system and the price recommended by the specific module.
 20. The process of claim 19 wherein the process recommends a final price for a product that minimizes the sum of the losses from all of said modules.
 21. The process of claim 19 wherein said loss function is Loss=(|Price(recommended[overall])-Price(recommended[module])|-Tolerance)ˆL {Min 0} where Price (recommended[overall]) is the price based on the weighted average of all modules, Price (recommended[module]) is the recommended price from one specific MBOS analytic module, Tolerance is the acceptable distance between the two prices (defined by the user), L is the loss exponent, adjustable within the software based on the preference of the user-retailer, and {Min 0} means that loss cannot be below zero.
 22. A price optimization process for recommending product price changes to a user, said process recommending price changes to a plurality of products, determining the numerical endings of the prices for at least some of said recommended price changes, and providing suggested price adjustments based on said numerical endings.
 23. The system of claim 22 wherein said suggested price adjustments for numerical endings between $0.50 and $1.00 are selected from the following table according to price ending value: Price Ending Value Price Ending (Last Two Digits) Adjustment (Add) 50 0 51 0 52 0 53 0 54 0 55 0 56 0 57 0 58 0.01 59 0 60 −0.01 61 0.04 62 0.03 63 0.02 64 0.01 65 0 66 0.03 67 0.02 68 0.01 69 0 70 −0.01 71 0.04 72 0.03 73 0.02 74 0.01 75 0 76 0.03 77 0.02 78 0.01 79 0 80 −0.01 81 −0.02 82 0.07 83 0.06 84 0.05 85 0.04 86 0.03 87 0.02 88 0.01 89 0 90 −0.01 91 0.07 92 0.06 93 0.06 94 0.05 95 0.04 96 0.03 97 0.02 98 0.01 99 0


24. The system of claim 22 wherein said suggested price adjustments for numerical endings above $1.00 are selected from the following table according to price ending value: Price Ending Value Price Ending (Last Two Digits) Adjustment (Add) 01 −0.02 02 −0.03 03 −0.04 04 0.05 05 0.04 06 0.03 07 0.02 08 0.01 09 0 10 0.09 11 0.08 12 0.07 13 0.06 14 0.05 15 0.04 16 0.03 17 0.02 18 0.01 19 0 20 −0.01 21 0.08 22 0.07 23 0.06 24 0.05 25 0.04 26 0.03 27 0.02 28 0.01 29 0 30 −0.01 31 −0.02 32 −0.03 33 0.06 34 0.05 35 0.04 36 0.03 37 0.02 38 0.01 39 0 40 0.09 41 0.08 42 0.07 43 0.06 44 0.05 45 0.04 46 0.03 47 0.02 48 0.01 49 0 50 −0.01 51 −0.02 52 −0.03 53 −0.04 54 −0.05 55 −0.06 56 −0.07 57 0.02 58 0.01 59 0 60 −0.01 61 0.08 62 0.07 63 0.06 64 0.05 65 0.04 66 0.03 67 0.02 68 0.01 69 0 70 0.09 71 0.08 72 0.07 73 0.06 74 0.05 75 0.04 76 0.03 77 0.02 78 0.01 79 0 80 −0.01 81 −0.02 82 −0.03 83 0.06 84 0.05 85 0.04 86 0.03 87 0.02 88 0.01 89 0 90 −0.01 91 0.08 92 0.07 93 0.06 94 0.05 95 0.04 96 0.03 97 0.02 98 0.01 99 0 